利用k阶最近邻统计量从非线性尺度进行稳健宇宙学推断
Robust cosmological inference from non-linear scales with k-th nearest neighbor statistics
AI总结：
本研究提出利用kNN统计量从非线性尺度开展宇宙学推断的方法，通过交叉验证确定稳健星系-晕模型，实现sigma_8 2%的无偏约束，对多种模型系统误差具有鲁棒性，已在Beyond-2p模拟挑战中验证有效。
摘要：
我们提出了一种利用k阶最近邻（kNN）统计量从非线性尺度（<50Mpc/h）获取准确可靠宇宙学约束的方法。我们详细介绍了选择稳健最小尺度截断以及验证星系-晕连接模型的方法。通过交叉验证，我们确定了能在多种汇总统计量下同时保证良好拟合和无偏预测的星系-晕模型。研究表明，我们可以有效建模低至横向尺度rp~3Mpc/h的kNN，实现对物质密度和成团振幅的精确无偏约束，其中sigma_8的约束精度达2%。我们基于模拟的模型流程对各类模型系统误差（涵盖模拟代码、晕族查找和宇宙学先验）具有鲁棒性。我们通过在Beyond-2p模拟挑战中的应用验证了该方法的有效性，并提出将进一步探索更复杂的星系-晕连接模型，应对潜在的观测系统误差。
英文摘要：
We present the methodology for deriving accurate and reliable cosmological constraints from non-linear scales (<50Mpc/h) with k-th nearest neighbor (kNN) statistics. We detail our methods for choosing robust minimum scale cuts and validating galaxy-halo connection models. Using cross-validation, we identify the galaxy-halo model that ensures both good fits and unbiased predictions across diverse summary statistics. We demonstrate that we can model kNNs effectively down to transverse scales of rp ~ 3Mpc/h and achieve precise and unbiased constraints on the matter density and clustering amplitude, leading to a 2% constraint on sigma_8. Our simulation-based model pipeline is resilient to varied model systematics, spanning simulation codes, halo finding, and cosmology priors. We demonstrate the effectiveness of this approach through an application to the Beyond-2p mock challenge. We propose further explorations to test more complex galaxy-halo connection models and tackle potential observational systematics.
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